Make predictions from an existing prediction model

Use an existing prediction model to estimate predicted risks of the outcome for each observation in a new dataset.

Usage

pred_predict(
  x,
  new_data,
  binary_outcome = NULL,
  survival_time = NULL,
  event_indicator = NULL,
  time_horizon = NULL
)

Arguments

x: an object of class "predinfo" produced by calling pred_input_info.
new_data: data.frame upon which predictions are obtained using the prediction model.
binary_outcome: Character variable giving the name of the column in new_data that represents the observed outcomes. Only relevant for model_type="logistic"; leave as NULL otherwise. Leave as NULL if new_data does not contain any outcomes.
survival_time: Character variable giving the name of the column in new_data that represents the observed survival times. Only relevant for model_type="survival"; leave as NULL otherwise. Leave as NULL if new_data does not contain any survival outcomes.
event_indicator: Character variable giving the name of the column in new_data that represents the observed survival indicator (1 for event, 0 for censoring). Only relevant for model_type="survival"; leave as NULL otherwise. Leave as NULL if new_data does not contain any survival outcomes.
time_horizon: for survival models, an integer giving the time horizon (post baseline) at which a prediction is required (i.e. the t at which P(T<t) should be estimated). Currently, this must match a time in x$cum_hazard. If left as NULL, no predicted risks will be returned, just the linear predictor.

Value

pred_predict returns a list containing the following components:

LinearPredictor = the linear predictor for each observation in the new data (i.e., the linear combination of the models predictor variables and their corresponding coefficients)
PredictedRisk = the predicted risk for each observation in the new data
TimeHorizon = for survival models, an integer giving the time horizon at which a prediction is made
Outcomes = vector of outcomes/endpoints (if available).

Details

This function takes the relevant information about the existing prediction model (as supplied by calling pred_input_info), and returns the linear predictor and predicted risks for each individual/observation in new_data.

If the existing prediction model is based on logistic regression (i.e., if x$model_type == "logistic"), the predicted risks will be the predicted probability of the binary outcome conditional on the predictor variables in the new data (i.e., P(Y=1 | X)). If the existing prediction model is based on a time-to-event/survival model (i.e., if x$model_type == "survival"), the predicted risks can only be calculated if a baseline cumulative hazard is provided; in this case, the predicted risks will be one minus the survival probability (i.e., 1 - S(T>time horizon | X)).

new_data should be a data.frame, where each row should be an observation (e.g. patient) and each variable/column should be a predictor variable. The predictor variables need to include (as a minimum) all of the predictor variables that are included in the existing prediction model (i.e., each of the variable names supplied to pred_input_info, through the model_info parameter, must match the name of a variables in new_data).

Any factor variables within new_data must be converted to dummy (0/1) variables before calling this function. dummy_vars can help with this.

binary_outcome, survival_time and event_indicator are used to specify the outcome variable(s) within new_data (use binary_outcome if x$model_type = "logistic", or use survival_time and event_indicator if x$model_type = "survival").

Examples